Genomic Signal Processing

Ilya Shmulevich; Edward R. Dougherty

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Princeton University Press

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Book

Genomic Signal Processing

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Language: English

Published/Copyright: 2014

About this book

Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathematical definitions and propositions for the main elements of GSP and by paying attention to the validity of models relative to the data. Ilya Shmulevich and Edward Dougherty cover real-world situations and explain their mathematical modeling in relation to systems biology and systems medicine.

Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention.

Author / Editor information

Ilya Shmulevich, an associate professor at the Institute for Systems Biology, is the coauthor of Microarray Quality Control and the coeditor of Computational and Statistical Approaches to Genomics. Edward R. Dougherty is professor of electrical and computer engineering and director of the Genomic Signal Processing Laboratory at Texas A&M University, and director of the Computational Biology Division at the Translational Genomics Research Institute. His thirteen previous books include Random Processes for Image and Signal Processing.

Reviews

"Overall, this book should be useful for individuals with a background in computer science and machine learning who wish to see the applications of mathematics to genomics."---Leon Glass, SIAM Review --- "Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention. . . . The authors' substantial accomplishments in this area will inspire researchers and students alike. The book provides a much-needed stepping stone so that researchers can cross the gap on this front in their efforts. Also assuredly, it will be a delight to read for anyone who is encountering the topic for the first time and is wishing to exploit the current findings and interpretations in systems biology."

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Frontmatter
i

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Contents
v

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Preface
ix

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Chapter One. Biological Foundations
1

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Chapter Two. Deterministic Models of Gene Networks
23

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Chapter Three. Stochastic Models of Gene Networks
77

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Chapter Four. Classification
160

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Chapter Five. Regularization
225

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Chapter Six. Clustering
262

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Index
295

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Publishing information

Pages and Images/Illustrations in book

eBook published on:

September 8, 2014

eBook ISBN:

9781400865260

Pages and Images/Illustrations in book

https://doi.org/10.1515/9781400865260

eBook ISBN: 9781400865260

Audience(s) for this book

For an expert adult audience, including professional development and academic research

Princeton University Press

Genomic Signal Processing

Overview

About this book

Author / Editor information

Reviews

Topics

Table of contents

Frontmatter

Contents

Preface

Chapter One. Biological Foundations

Chapter Two. Deterministic Models of Gene Networks

Chapter Three. Stochastic Models of Gene Networks

Chapter Four. Classification

Chapter Five. Regularization

Chapter Six. Clustering

Index

Bibliographic data